Neural network based approaches have been widely used for a classification task. A task of retrieving a document that has an answer to a query can be seen as a natural language query (NLQ) classification task. For the NLQ classification task, pairs of a query and a correct document label identifying a document that includes an answer for the query are used for training a classification model. The trained classification model can detect an appropriate document label for a new unseen query by using features of the trained model and the new query.
Some portions of the training queries may have multiple labels for a single instance of the training queries, i.e., label co-occurrence may happen. Thus, the NLQ classification task in nature requires multi-label classification where multiple labels can be assigned to a single instance of training queries and multiple labels can be predicted for a new query. In such multi-label classification, dependency and relationship between the labels need to be taken in consideration. The neural networks can be used for the multi-label classification, also known as a back-propagation multi-label learning (BP-MLL). Recently, replacing BP-MLL's pairwise ranking loss with cross entropy error function has been suggested for efficient text classifications (J. Nam et al., Large-scale Multi-label Text Classification—Revisiting Neural Networks, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 437-452, 2014.).
However, there is no known technique that can leverage label co-occurrence information more directly in the learning of the classification models.    [Non-Patent Literature 1] J. Nam et al., Large-scale Multi-label Text Classification—Revisiting Neural Networks, In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 437-452, 2014.